Europeans' support for refugees of varying background is stable over time.

Protracted global conflicts during the past decade have led to repeated major humanitarian protection crises in Europe. During the height of the Syrian refugee crisis at the end of 2015, Europe hosted around 2.3 million people requesting asylum1. Today, the ongoing war in Ukraine has resulted in one of the largest humanitarian emergencies in Europe since World War II, with more than eight million Ukrainians seeking refuge across Europe2. Here we explore whether repeated humanitarian crises threaten to exhaust solidarity and whether Europeans welcome Ukrainian asylum seekers over other asylum seekers3,4. We conducted repeat conjoint experiments during the 2015-2016 and 2022 refugee crises, asking 33,000 citizens in 15 European countries to evaluate randomly varied profiles of asylum seekers. We find that public preferences for asylum seekers with specific attributes have remained remarkably stable and general support has, if anything, increased slightly over time. Ukrainian asylum seekers were welcomed in 2022, with their demographic, religious and displacement profile having a larger role than their nationality. Yet, this welcome did not come at the expense of support for other marginalized refugee groups, such as Muslim refugees. These findings have implications for our theoretical understanding of the drivers and resilience of public attitudes towards refugees and for policymakers tasked to find effective responses to the enduring stress on the asylum system5-8.

Empathy-based counterspeech can reduce racist hate speech in a social media field experiment.

Despite heightened awareness of the detrimental impact of hate speech on social media platforms on affected communities and public discourse, there is little consensus on approaches to mitigate it. While content moderation-either by governments or social media companies-can curb online hostility, such policies may suppress valuable as well as illicit speech and might disperse rather than reduce hate speech. As an alternative strategy, an increasing number of international and nongovernmental organizations (I/NGOs) are employing counterspeech to confront and reduce online hate speech. Despite their growing popularity, there is scant experimental evidence on the effectiveness and design of counterspeech strategies (in the public domain). Modeling our interventions on current I/NGO practice, we randomly assign English-speaking Twitter users who have sent messages containing xenophobic (or racist) hate speech to one of three counterspeech strategies-empathy, warning of consequences, and humor-or a control group. Our intention-to-treat analysis of 1,350 Twitter users shows that empathy-based counterspeech messages can increase the retrospective deletion of xenophobic hate speech by 0.2 SD and reduce the prospective creation of xenophobic hate speech over a 4-wk follow-up period by 0.1 SD. We find, however, no consistent effects for strategies using humor or warning of consequences. Together, these results advance our understanding of the central role of empathy in reducing exclusionary behavior and inform the design of future counterspeech interventions.

Monitoring hiring discrimination through online recruitment platforms.

Women (compared to men) and individuals from minority ethnic groups (compared to the majority group) face unfavourable labour market outcomes in many economies1,2, but the extent to which discrimination is responsible for these effects, and the channels through which they occur, remain unclear3,4. Although correspondence tests5-in which researchers send fictitious CVs that are identical except for the randomized minority trait to be tested (for example, names that are deemed to sound 'Black' versus those deemed to sound 'white')-are an increasingly popular method to quantify discrimination in hiring practices6,7, they can usually consider only a few applicant characteristics in select occupations at a particular point in time. To overcome these limitations, here we develop an approach to investigate hiring discrimination that combines tracking of the search behaviour of recruiters on employment websites and supervised machine learning to control for all relevant jobseeker characteristics that are visible to recruiters. We apply this methodology to the online recruitment platform of the Swiss public employment service and find that rates of contact by recruiters are 4-19% lower for individuals from immigrant and minority ethnic groups, depending on their country of origin, than for citizens from the majority group. Women experience a penalty of 7% in professions that are dominated by men, and the opposite pattern emerges for men in professions that are dominated by women. We find no evidence that recruiters spend less time evaluating the profiles of individuals from minority ethnic groups. Our methodology provides a widely applicable, non-intrusive and cost-efficient tool that researchers and policy-makers can use to continuously monitor hiring discrimination, to identify some of the drivers of discrimination and to inform approaches to counter it.

The effect of citizenship on the long-term earnings of marginalized immigrants: Quasi-experimental evidence from Switzerland.

We provide evidence that citizenship catalyzes the long-term economic integration of immigrants. Despite the relevance of citizenship policy to immigrant integration, we lack a reliable understanding of the economic consequences of acquiring citizenship. To overcome nonrandom selection into naturalization, we exploit the quasi-random assignment of citizenship in Swiss municipalities that held referendums to decide the outcome of individual naturalization applications. Our data combine individual-level referendum results with detailed social security records from the Swiss authorities. This approach allows us to compare the long-term earnings of otherwise similar immigrants who barely won or lost their referendum. We find that winning Swiss citizenship in the referendum increased annual earnings by an average of approximately 5000 U.S. dollars over the subsequent 15 years. This effect is concentrated among more marginalized immigrants.

Ethnic networks can foster the economic integration of refugees.

There is widespread concern in Europe and other refugee-receiving continents that living in an enclave of coethnics hinders refugees' economic and social integration. Several European governments have adopted policies to geographically disperse refugees. While many theoretical arguments and descriptive studies analyze the impact of spatially concentrated ethnic networks on immigrant integration, there is limited causal evidence that sheds light on the efficacy of these policies. We provide evidence by studying the economic integration of refugees in Switzerland, where some refugees are assigned to live in a specific location upon arrival and, by law, are not permitted to relocate during the first 5 y. Leveraging this exogenous placement mechanism, we find that refugees assigned to locations with many conationals are more likely to enter the labor market. This benefit is most pronounced about 3 y after arrival and weakens somewhat with longer residency. In addition, we find that, among refugees employed by the same company, a high proportion share nationality, ethnicity, or language, which suggests that ethnic residential networks transmit information about employment opportunities. Together, these findings contribute to our understanding of the importance of ethnic networks for facilitating refugee integration, and they have implications for the design of refugee allocation policies.

Multidimensional measure of immigrant integration.

The successful integration of immigrants into a host country's society, economy, and polity has become a major issue for policymakers in recent decades. Scientific progress in the study of immigrant integration has been hampered by the lack of a common measure of integration, which would allow for the accumulation of knowledge through comparison across studies, countries, and time. To address this fundamental problem, we propose the Immigration Policy Lab (IPL) Integration Index as a pragmatic and multidimensional measure of immigrant integration. The measure, both in the 12-item short form (IPL-12) and the 24-item long form (IPL-24), captures six dimensions of integration: psychological, economic, political, social, linguistic, and navigational. The measure can be used across countries, over time, and across different immigrant groups and can be administered through short questionnaires available in different modes. We report on four surveys we conducted to evaluate the empirical performance of our measure. The tests reveal that the measure distinguishes among immigrant groups with different expected levels of integration and also correlates with well-established predictors of integration.

The long-term impact of employment bans on the economic integration of refugees.

Many European countries impose employment bans that prevent asylum seekers from entering the local labor market for a certain waiting period upon arrival. We provide evidence on the long-term effects of these employment bans on the subsequent economic integration of refugees. We leverage a natural experiment in Germany, where a court ruling prompted a reduction in the length of the employment ban. We find that, 5 years after the waiting period was reduced, employment rates were about 20 percentage points lower for refugees who, upon arrival, had to wait for an additional 7 months before they were allowed to enter the labor market. It took up to 10 years for this employment gap to disappear. Our findings suggest that longer employment bans considerably slowed down the economic integration of refugees and reduced their motivation to integrate early on after arrival. A marginal social cost analysis for the study sample suggests that this employment ban cost German taxpayers about 40 million euros per year, on average, in terms of welfare expenditures and foregone tax revenues from unemployed refugees.

Improving refugee integration through data-driven algorithmic assignment.

Developed democracies are settling an increased number of refugees, many of whom face challenges integrating into host societies. We developed a flexible data-driven algorithm that assigns refugees across resettlement locations to improve integration outcomes. The algorithm uses a combination of supervised machine learning and optimal matching to discover and leverage synergies between refugee characteristics and resettlement sites. The algorithm was tested on historical registry data from two countries with different assignment regimes and refugee populations, the United States and Switzerland. Our approach led to gains of roughly 40 to 70%, on average, in refugees' employment outcomes relative to current assignment practices. This approach can provide governments with a practical and cost-efficient policy tool that can be immediately implemented within existing institutional structures.

How economic, humanitarian, and religious concerns shape European attitudes toward asylum seekers.

What types of asylum seekers are Europeans willing to accept? We conducted a conjoint experiment asking 18,000 eligible voters in 15 European countries to evaluate 180,000 profiles of asylum seekers that randomly varied on nine attributes. Asylum seekers who have higher employability, have more consistent asylum testimonies and severe vulnerabilities, and are Christian rather than Muslim received the greatest public support. These results suggest that public preferences over asylum seekers are shaped by sociotropic evaluations of their potential economic contributions, humanitarian concerns about the deservingness of their claims, and anti-Muslim bias. These preferences are similar across respondents of different ages, education levels, incomes, and political ideologies, as well as across the surveyed countries. This public consensus on what types of asylum seekers to accept has important implications for theory and policy.

When lives are put on hold: Lengthy asylum processes decrease employment among refugees.

European governments are struggling with the biggest refugee crisis since World War II, but there exists little evidence regarding how the management of the asylum process affects the subsequent integration of refugees in the host country. We provide new causal evidence about how one central policy parameter, the length of time that refugees wait in limbo for a decision on their asylum claim, affects their subsequent economic integration. Exploiting exogenous variation in wait times and registry panel data covering refugees who applied in Switzerland between 1994 and 2004, we find that one additional year of waiting reduces the subsequent employment rate by 4 to 5 percentage points, a 16 to 23% drop compared to the average rate. This deleterious effect is remarkably stable across different subgroups of refugees stratified by gender, origin, age at arrival, and assigned language region, a pattern consistent with the idea that waiting in limbo dampens refugee employment through psychological discouragement, rather than a skill atrophy mechanism. Overall, our results suggest that marginally reducing the asylum waiting period can help reduce public expenditures and unlock the economic potential of refugees by increasing employment among this vulnerable population.

Examination of the Synthetic Control Method for Evaluating Health Policies with Multiple Treated Units.

This paper examines the synthetic control method in contrast to commonly used difference-in-differences (DiD) estimation, in the context of a re-evaluation of a pay-for-performance (P4P) initiative, the Advancing Quality scheme. The synthetic control method aims to estimate treatment effects by constructing a weighted combination of control units, which represents what the treated group would have experienced in the absence of receiving the treatment. While DiD estimation assumes that the effects of unobserved confounders are constant over time, the synthetic control method allows for these effects to change over time, by re-weighting the control group so that it has similar pre-intervention characteristics to the treated group. We extend the synthetic control approach to a setting of evaluation of a health policy where there are multiple treated units. We re-analyse a recent study evaluating the effects of a hospital P4P scheme on risk-adjusted hospital mortality. In contrast to the original DiD analysis, the synthetic control method reports that, for the incentivised conditions, the P4P scheme did not significantly reduce mortality and that there is a statistically significant increase in mortality for non-incentivised conditions. This result was robust to alternative specifications of the synthetic control method. © 2015 The Authors. Health Economics published by John Wiley & Sons Ltd.

Naturalization fosters the long-term political integration of immigrants.

Does naturalization cause better political integration of immigrants into the host society? Despite heated debates about citizenship policy, there exists almost no evidence that isolates the independent effect of naturalization from the nonrandom selection into naturalization. We provide new evidence from a natural experiment in Switzerland, where some municipalities used referendums as the mechanism to decide naturalization requests. Balance checks suggest that for close naturalization referendums, which are decided by just a few votes, the naturalization decision is as good as random, so that narrowly rejected and narrowly approved immigrant applicants are similar on all confounding characteristics. This allows us to remove selection effects and obtain unbiased estimates of the long-term impacts of citizenship. Our study shows that for the immigrants who faced close referendums, naturalization considerably improved their political integration, including increases in formal political participation, political knowledge, and political efficacy.

Validating vignette and conjoint survey experiments against real-world behavior.

Survey experiments, like vignette and conjoint analyses, are widely used in the social sciences to elicit stated preferences and study how humans make multidimensional choices. However, there is a paucity of research on the external validity of these methods that examines whether the determinants that explain hypothetical choices made by survey respondents match the determinants that explain what subjects actually do when making similar choices in real-world situations. This study compares results from conjoint and vignette analyses on which immigrant attributes generate support for naturalization with closely corresponding behavioral data from a natural experiment in Switzerland, where some municipalities used referendums to decide on the citizenship applications of foreign residents. Using a representative sample from the same population and the official descriptions of applicant characteristics that voters received before each referendum as a behavioral benchmark, we find that the effects of the applicant attributes estimated from the survey experiments perform remarkably well in recovering the effects of the same attributes in the behavioral benchmark. We also find important differences in the relative performances of the different designs. Overall, the paired conjoint design, where respondents evaluate two immigrants side by side, comes closest to the behavioral benchmark; on average, its estimates are within 2% percentage points of the effects in the behavioral benchmark.